FUTY Journal of the Environment Vol. 14 No. 1 March, 2020

1

Spatio-temporal Variability of Landuse Landcover and its Impact on Land

Surface Temperature in Zaria Metropolis, Nigeria

*aAzua, S., bNnah, S. I. and aIkwueze, H. U. aDepartment of Geomatics, Faculty of Environmental Design,

Ahmadu Bello University, Zaria, Nigeria

bDepartment of Surveying and Geoinformatics,

Akanu Ibiam Federal Polytechnics, Afikpo, Ebonyi State, Nigeria *Correspondence email: adzuasamuel@yahoo.com

Abstract

This paper assessed the spatio-temporal variability of land use land cover (LULC) and its

impact on Land Surface Temperature (LST) in Zaria and environs. Multi-temporal Landsat

data; Landsat 5TM, 7ETM+ and 8OLI of 1988, 2003 and 2018, respectively, at an interval of

15 years were obtained. These data were processed and classified into various classes using

the supervised classification. It was also used to determine the LST of the area. The results of

the classification revealed that, apart from built-up area which increased consistently from

15.353 km2 in 1988 to 32.8623km2 in 2018, all other LULC decreased within the study period.

The LST ranges from 1.5 to 35.9ºC across the study period. The relationship between LULC

and LST was investigated and result showed that LST had positive correlation of 0.608 with

built-up area indicating that LST increased with increase in built-up areas. However, dense

vegetation, light vegetation and bare land had negative correlations of -0.976, -0.851 and -

0.708, respectively, with LST indicating that LST increase with decrease in vegetation and bare

land. The implication of this unprecedented changes is the resulting environmental and

climatic problems such as urban heat island and desertification which have become very

common in the study area. It was suggested that the LULC of Zaria metropolis should be

controlled and afforestation be encouraged to enhance a healthy living condition of the area.

Keywords: Land Surface Temperature, Land Use Land Cover Change, Landsat

INTRODUCTION

The earth surface continuously undergo changes propelled by both natural and anthropogenic

activities (Fall et al., 2009). Anthropogenic activities include farming, cattle rearing,

settlements and industry amongst others (Azua, 2018b). These activities otherwise refer to as

land uses by most researchers (Kang et al., 2010; Zhang et al., 2010; Bu et al., 2014; Azua,

2018a) are on the increase due to increase in the human population across the globe. Nigeria

for instance had a population of 140 million in 2006 (NPC, 2006). Today, her population has

risen to over 190 million (USCB, 2019). This has led to the expansion in settlements, farmland

and deforestation as well as increase in demand for food, water and other natural resources.

This result in series of environmental and climatic consequences that affects the wellbeing of

man, plants and animals as well as the ecosystem in general.

Land Surface Temperature (LST) is one of the climatic parameters that is affected by changes

in land use land cover. It refers to the temperature of the air near the surface of earth (Saini and

Tiwari, 2017). Increase in LST can lead to an increase in urban heat island which causes an

urban area to be warmer than its surrounding rural areas (Voogt and Oke, 2003).

FUTY Journal of the Environment Vol. 14 No. 1 March, 2020

2

There is need to assess the changes LULC from time to time because these changes are very

vital in the monitoring and management of environment and its resources, for human wellbeing

(Oyinloye and Kufoniyi, 2011; Nzoiwu et al., 2017; Daramola and Eresanya, 2017).

Several studies have been conducted on the analysis of LULC of Zaria. Azua (2010), Grace et

al. (2015) and Okewu (2016) studied the changing pattern of LULC in Zaria metropolis.

However, it appears that no study assessing the impact of LULC on LST has been carried out

in Zaria. The aim of this study therefore, is to analyse the impact of anthropogenic activities

on LST in Zaria and its environs, with the view of providing better understanding on the

biophysical composition of the earth for effective planning and management. The objectives

of this study are to; determine the land use land cover dynamics and LST in the study area;

determine the link between land use land cover dynamics and LST in the area; and to discuss

the implications and suggest possible ways of reducing the impacts of these changes on the

populace.

The Study Area

Zaria is the second largest city in Kaduna State located in northern parts of the state. It lies

within Latitude 10°5' and 11°6' North of the equator, and Longitude 7°4' and 8°5' east of the

Greenwich meridian (Figure 1). Zaria is located on the central plains of the Hausa high land

standing at a height of about 670m above Mean Sea Level (Azua, 2010). It is drained by

Kubanni, Galma and Saye Rivers all of which converge on River Kaduna. Zaria comprises of

two Local Government Areas (LGAs) namely Sabon Gari and Zaria LGAs and has a population

of about 1, 364,942 (Okewu, 2016). The study area has many institutions and industries which

have attracted many people from different parts of the country to the area in search of shelter,

better education and white-collar jobs leading to high population (Kugu, 2018).

Figure 1: The Study Area.

FUTY Journal of the Environment Vol. 14 No. 1 March, 2020

3

The climate of the area is tropical continental comprising of two seasons namely, wet and dry

seasons (Azua, 2010; Grace et al., 2015). The wet season extend from April to October having

an average of about 1100mm per annum. The dry season extend from November to March

having daily maximum temperature rising from 33°C in January to the peak of about 40.6°C

in April (Azua, 2010). The vegetation of the area was forest savannah however, due to

settlement expansion, agricultural activities and other land uses, the natural vegetation has

changed to guinea savannah.

The economic activities of the indigenes were primary production which involves the direct

exploitation of the natural resources for immediate use. Hence, activities such as fishing,

farming, hunting and mining of iron ore for the production of tools were very common in the

area. These activities have changed tremendously due to urbanization.

MATERIALS AND METHODS

Materials

The Landsat data acquired for this study include Landsat 5 Thematic Mapper (TM), Landsat 7

Enhance Thematic Mapper plus (ETM+) and Landsat 8 Operational Land Imager (OLI). The

data were obtained from the data archive of the United States Geological Survey (USGS)

(glovis.usgs.gov) with the following characteristics as stated in Table 1. All the satellite images

were acquired in January to avoid cloud cover and to ensure high accuracy. The images were

acquired for a period of 30 years at an interval of 15 years. These images were registered and

geo-corrected before been made available for use by the public (Azua et al., 2018b).

Table 1: Data Types and Sources

Data Types Path/Row Bands used Year Resolution Date acquired by Satellite

Landsat 5 TM P189/R52 2, 3 & 4 1988 30 X 30 12th January, 1988

Landsat 7 ETM+ P189/R52 2, 3 & 4 2003 30 X 30 27th January, 2003

Landsat 8 OLI P189/R52 3, 4 & 5 2018 30 X 30 21st January, 2018

Methods

Land Use Land Cover Classes

After forming the False Colour Composites (FCCs), the images were clipped to the boundary

of the study area and subjected to digital image processing using histogram equalization, to

enhance the image contrast. Supervised classification was employed to classify the FCCs into

various classes based on their spectral properties using maximum likelihood algorithm in

ERDAS Imagine 9.2 environment. The results were subjected to accuracy assessment using

error matrix and kappa coefficient to ensure that the results meet the required standard for

classification.

Extraction of Temperature Data

LST was extracted from Landsat 5 and 7 using bands 6 and Landsat 8 OLI using TIRS band

10. The top of atmospheric (TOA) spectral radiance (𝐿) was computed using equation 1 (Barsi

et al., 2014; Avdan and Jovanovska, 2016):

(a) Conversion from Digital Number to Spectral Radiance Eqn. 1 is applicable to only Landsat 5 TM and 7 ETM+ while equation 2 is applicable to

Landsat 8OLI

FUTY Journal of the Environment Vol. 14 No. 1 March, 2020

4

max min max min

max min

( ) ( )

( )

cal cal

cal cal

L L Q QL (1)

Q Q

calmax calmin

-2 -1Where L is the Spectral radiance at the sensor aperture (watt m ster ), L is the Spectral radiance max-2 -1 -2scaled to Q (watt m ster ), L is the Spectral radiance scaled to Q (watt m min

-1ster ),

Q is the Quantized calibrated pixel value DN, Q is the Minimum quantized calibrated pixel cal calmin

value corresponding to L , Q is the Maximum quantized calibrated pixel value correspomin calmaxnding

to L , L and L value was acquired from Landsat post callibration dynamic range tablemax max min

L cal L iL= M * Q + A -O (2)

L cal L

i

where, M is the band - specific multiplicative rescaling factor, Q is the Band 10 image, A is the

band - specific additive rescaling factor, and O is the correction for Band 10.

(b) Conversion from Spectral Radiance to Reflectance (at-satellite reflectance)

2

sun

L dr (3)

E Cos dr

-2 -1 -1

where, r is the Planetery reflectance (unitless),L is the Spectral radiance at the sensor aperture

(watt m ster μm ), dr is the Inverse square of earth - sun distance (astronomical unit) Esun is

th -2e Mean solar exoatmospheric irradiances (watt m μm, ), θ is the Solar zenith angle (degree) and

d is the distance from the earth to the sun.

(c) Computing NDVI using Landsat bands in reflectance

NDVI is calculated using the following expression (Azua et al., 2018b; Alemu, 2019):

( )

( )

NIR REDNDVI (4)

NIR RED

where, RED is the visible red reflectance and NIR= Near Infrared reflectance

(d) Land surface emissivity

Alemu (2019), defined land surface emissivity (ɛλ) as the “ratio of energy emitted from a

natural material to that of a perfect emitter (black body) at the same temperature”. The author

further stated that a surface used for emissivity is assumed to consist of bare soil and vegetation

and the computation can be done using the mathematical expression in equation 5 as follows:

λ vλ v sλ v λ= + P + 1- P +C (5)

v s

v

where, is the band emissivity value for vegetation, is the band emissivity value for bare soil, C is the

surface roughness taken as a constant value of 0.005, P = proportional vegetation that sho

ws the extent

of vegetation cover

FUTY Journal of the Environment Vol. 14 No. 1 March, 2020

5

vp can be derived from NDVI as follows:

( )

( )

v sv

v s

NDVI NDVIP (6)

NDVI NDVI

v s

v s

where,NDVI is the NDVI value for full vegetation cover, NDVI is the NDVI value for bare soil.

The NDVI and NDVI of 0.5 and 0.2 were adopted as contained in Alemu (2019).

(e) Computation of Temperature Data

LST was computed from Landsat 5 and 7 using bands 6 and Landsat 8 OLI using TIRS band

10. The top of atmospheric (TOA) spectral radiance ( L ) was computed using equation 2

(Barsi et al., 2014; Avdan and Jovanovska, 2016).

(f) Conversion of Radiance to At-Sensor Temperature

The TIRS band data was converted from spectral radiance to brightness temperature (BT) using

the thermal constants provided in the metadata file as shown in equation 7 (Avdan and

Jovanovska, 2016):

2

1

KBT = - 273.15 (7)

ln k / Lλ +1

1 2where, k and k are the specific thermal conversion constants from the metadata. (For obtaining

the results in Celsius, the radiant temperature is revised by adding the absolute zero

(approx. - 273.15°C))

The last step of retrieving the LST or the emissivity-corrected land surface temperature is

computed as follows:

1 ( / ) ln )

s

BTT (8)

T

s

λ

where, T is the LST in Celsius °C , BT is at sensor BT °C , λ = the wavelength of emmitted

radiance (for which the peak response and the average of the limiting wavelength (λ = 10.895)

will be used) and ε is the emissivity calculated.

RESULTS AND DISCUSSION

Land Use Land Cover

The result identified five (5) LULC classes in the area namely, built-up, dense vegetation, light

vegetation, bare lands and water body as shown in Figures 2,a-c. Table 2 showed the

breakdown of various classes in each year of study. The accuracy assessment showed that, the

overall accuracy for Landsat 5, 7 and 8 were 82%, 85% and 90%, respectively while the Kappa

coefficients were 75%, 78% and 92% in the same order.

FUTY Journal of the Environment Vol. 14 No. 1 March, 2020

6

Table 2 revealed that built-up area has been on the increase, expanding from 15.353 km2 in

1988 to 23.030 km2 in 2003 and 32.862 km2 in 2018. This may be attributed to the influx of

people in the area due to change in the socio-economic condition of the area. This result agrees

with Azua (2010) who reported similar findings in the area. Some of the areas that witnessed

expansion in built-up include PZ, Zaria City, Sabon Gari, Grace Land and Kabama Layout

amongst others. Dense vegetation on the other hand increased from 21.950 Km2 in 1988 to

34.675 km2 in 2003 gaining about 9.8 km2 and then decreased to 14.801 km2 in 2018. The

increase and decrease in dense vegetation observed between 1988 and 2018 seems abnormal,

however, it could be due to various anthropogenic activities associated with the increase in

Table 2: Land Use Land Cover Class Name 1988 (Km2) Percentage

(%)

2003

(Km2)

Percentage

(%)

2018

(Km2)

Percentage

(%)

Unclassified 134.443 44.3 134.439 44.3 202.745 66.9

Built-up 15.353 5.1 23.030 7.6 32.862 10.8

Water Body 0.929 0.3 0.489 0.2 1.306 0.4

Dense Veg. 21.95 7.2 34.675 11.4 14.801 4.9

Light Veg. 50.787 16.8 48.397 16.0 26.535 8.7

Bare Land 79.825 26.3 62.248 20.5 25.028 8.3

TOTAL 303.277 100 303.277 100 303.277 100

population and expansion in settlement within the study area. This agrees with Okewu (2016)

who reported similar findings in Zaria. Light vegetation also experienced decrease from

FUTY Journal of the Environment Vol. 14 No. 1 March, 2020

7

50.787 km2 in 1988 to 48.397 km2 in 2003 and 26.535km2 in 2018 losing about 21.800 km2.

This may not be unconnected with the increase in population and the demand for fuel wood

and agricultural activities amongst others.

Figure 3: Graphical Representation of Land Use Land Cover of Zaria

The areas affected are mostly those that witness expansion in settlement and agricultural

activities. The increase in population of Zaria could be attributed to the expansion in both

governmental and non-governmental organizations, including banks, manufacturing firms,

service sectors, and educational institutions. Tertiary institutions such as the Ahmadu Bello

University, Zaria, Nigerian College of Aviation Technology, Zaria and Nigerian Institute of

Transport Technology, Zaria amongst others have attracted many people in Zaria which

increased the demand for land and other natural resources in the area.

Similarly, bare lands also decreased from 79.825 km2 in 1988 to 62.248 km2 in 2003 and then

25.028 km2 in 2018. This may also be attributed to the expansion in settlement and other

anthropogenic activities which converted the bare lands into other land uses. Additionally,

water body experienced decrease from 0.929 km2 in 1988 to 0.4887 km2 in 2003 and then

increased to 1.306 km2 in 2018. This may be due to the increase in demand for land used for

settlement, agriculture and other human activities in the area. This result is also similar to

Okewu (2016) who observed fluctuations in the water level in the area.

Spatio-temporal Analysis of LST

The result of LST for the study period is presented in Figures 4a-c. The spatial distribution of

LST of Zaria and environs showed temperature ranges between 14.74 and 25.58°C in 1988,

15.1 and 32.00°C in 2003 and, 16.55 and 35.94°C in 2018. Figure 4d revealed that, the area

covered by low temperature increased slightly from 1988 to 2003 and then decreased in 2018.

However, the area covered by moderate temperature decreased consistently from 1988 to 2003

and then to 2018, while that of high temperature on the other hand increase consistently from

1988 to 2003 and then to 2018. These changes may not be unconnected with the LULC

dynamics observed in the study area leading to decrease in area covered by low temperature

and increase in area covered by high temperature values. Thus, high temperature areas are

shown in the shades of red and correspond to built-up areas, bare lands and low vegetated areas.

Low temperature areas are shown in shades of green and correspond to vegetation and water

bodies.

Validation of LST

The LST obtained from Landsat data was compared with the ground-based method obtained

from Nigerian Meteorological Agency (NiMET), Institute of Agricultural Research (IAR),

FUTY Journal of the Environment Vol. 14 No. 1 March, 2020

8

Ahmadu Bello University, Zaria (Table 3). The comparison revealed that, the values are almost

the same, thus, indicating that, the LST obtained from Landsat data are accurate and reliable.

The slight differences observed might be due to radiometric error caused by atmospheric error

and satellite platform.

Analysis of Relationship between LULC and LST

It is a fact that built-up areas exhibit higher LST than vegetated areas (Amiri et al., 2009;

Takeuchi et al., 2010; Nzoiwu et al., 2017). Table 4 revealed that, the highest mean temperature

of 28.55ºC with standard deviation of 1.81 was obtained in 2018. This may not be unconnected

with the expanse of built-up area in the study area. This means that, the replacement of natural

vegetation with other surfaces such as cemented buildings, pavements, metal, tarred roads and

Table 3: Validation LST 1988 2003 2018

Satellite Ground based Satellite Ground based Satellite Ground based

Minimum 14.7 10.0 15.1 17.0 16.6 15.0

Maximum 25.6 25.0 32.0 31.0 35.9 31.0

FUTY Journal of the Environment Vol. 14 No. 1 March, 2020

9

concrete among others induce LST. This accounted for the variation of LST in Zaria and

environs, thus high LST values were notably observed in PZ, Sabo, Grace Land, Samaru and

Zango amongst others. It was also observed in Table 4 that, the mean LST dropped from

21.11ºC in 1988 to 15.06ºC in 2003. This may be due to the increase in dense vegetation from

21.95 km2 in 1988 to 34.675 km2 in 2003. Vegetation as it is already stated, reduces LST

through transpiration and evaporation. This explains why the decrease in light vegetation

correspond to the increase in LST from 1988 to 2003. Thus low LST values were observed at

Ahmadu Bello University, Zaria and Gaskiya Danmagaji communities, amongst others.

Table 4: LST Statistics Year Mean

Temp

(ºC)

Standard

Deviation

Built-up

(km2)

Dense

Vegetation

(km2)

Light

Vegetation

(km2)

Bare Land

(km2)

Water

Body

(km2)

1988 21.11 1.02 15.353 21.95 50.787 79.825 0.929

2003 15.06 1.55 23.0301 34.6752 48.3966 62.2476 0.4887

2018 28.55 1.81 32.8623 14.8014 26.5346 25.0283 1.3059

The correlation between LST and LULC showed that, built-up area is positively correlated

(0.608) with LST which implies that increase in built-up area results in high LST. This agrees

with Adeyeri and Okogbue (2014) who reported similar findings in Abuja, Nigeria. Further, it

was also observed that dense and light vegetation have strong negative correlation (-0.976 and

-0.851, respectively) with LST. This indicates that, pixels with high vegetation content have

low surface temperature values. This agrees with the findings of Daramola and Eresanya (2017)

who reported similar findings in Akure, Ondo State.

Implications of the study

The findings reveal that LULC dynamics have negative effect on LST by increasing the LST

of the area under study. This causes urban heat island that leads to high temperature values that

are unfavourable to human wellbeing. This increase the demand for power supply for use of

fans, air conditioners and other means of cooling the temperature. Unfortunately, the power

supply is very epileptic thus, not sufficient for use during heat period. This can lead to severe

health challenges that may even result in death. Hence the need to control the LULC of Zaria

and environs is emphasized and afforestation should be encouraged to attenuate the rate of

change of LST in the area.

CONCLUSION

This study has successfully analysed the relationship between LULC dynamics and LST in

Zaria. The results revealed that built-up area has increased consistently within the study period.

This causes significant increase in LST of the area. It was also established that vegetation is

decreasing in the area due to the increase in human activities which has resulted in the increase

in LST. Bare land also decreased consistently within the study period accounting for substantial

increase in LST. In general, apart from the built-up area, all other land uses in the area are

decreasing thus paving way for increase in LST. It is therefore recommended that LULC of

Zaria and environs should be controlled and afforestation should be increased to enhance a

healthy living condition of the area.

References

Adeyeri, O. E. and Okogbue, E. (2014). Effect of Land Use Land Cover on Surface

Temperature in Abuja Using Remote Sensing and Geographic Information System (GIS).

FUTY Journal of the Environment Vol. 14 No. 1 March, 2020

10

Proceedings of International Conference on Climate Change, and Sustainable Economic

Development, 9th – 12th November, 2014, Makurdi, Nigeria 175 -184.

Alemu, M. M. (2019). Analysis of Spatio-temporal Land Surface Temperature and Normalized

Difference Vegetation Index Changes in the Andassa Watershed, Blue Nile Basin,

Ethiopia. J. Resour. Ecol. 10(1): 77-85 DOI: 10.5814/j.issn.1674-764x.2019.01.010

Amiri, R., Weng, Q., Alimohammadi, A. and Alavipanah, S. K. (2009). Spatial–temporal

dynamics of land surface temperature in relation to fractional vegetation cover and land

use/cover in the Tabriz urban area, Iran. Remote Sensing of Environment, 113: 2606–

2617.

Avdan, U. and Jovanovska, G. (2016). Algorithm for Automated Mapping of Land Surface

Temperature Using LANDSAT 8 Satellite Data. Journal of Sensors, Volume 2016,

http://dx.doi.org/10.1155/2016/1480307

Azua, S. (2010). Impact of Deforestation on Land Use System in Zaria. Nigerian, Journal of

Surveying and Geoinformatics, a publication of Nigerian Institution of Surveyors, (NIS),

3(1): 32-41.

Azua, S. (2018a). Analysis of spatio-temporal Variability of Anthropogenic Disturbances in

River Mu Drainage Basin, Nigeria. Ph. D Thesis submitted to the School of Postgraduate

Studies, Ahmadu Bello University, Zaria, Nigeria.

Azua, S., Adewuyi, T. O., Ojigi, M. L., Mudiare, O. J. and Ikwueze, H. U. (2018b). Analysis

of Vegetation Cover Changes for Land Use Planning using Normalized Vegetation Index

(NDVI) Along River Mu Drainage Basin, Central Nigeria. Journal of Geography and

Development, a publication of the Department of Geography, Benue State University,

Makurdi, 8(2): 1008-1019. ISSN: 2006-0378.

Barsi J. A., Schott J. R., Hook S. J., Raqueno N. G., Markham B. L. and Radocinski R. G.

(2014). Landsat-8 Thermal Infrared Sensor (TIRS) Vicarious Radiometric Calibration,”

Remote Sensing, 6(11): 11607–11626. https://doi.org/10.3390/rs61111607

Bu, H., Tan, X., Li, S., Zhang, Q. (2014). Temporal and spatial variations of water quality in

the jinshui river of the south qinling mts., China. Ecotox. Environ. Safe, 73 (5): 907-913.

Daramola, M. and Eresanya, E. (2017). Land Surface Temperature Analysis over Akure.

Journal of Environment and Earth Science, 7(5): 97-105. ISSN 2224-3216

Fall, S., Niyogi, D., Gluhovsky, A., Pielke Sr, R. A., Kalnaye, E. and Rochon, G. (2009).

Impacts of land use land cover on temperature trends over the continental United States:

assessment using the North American Regional Reanalysis. International Journal of

Climatology. Published online in Wiley InterScience. DOI: 10.1002/joc.1996.

Grace, U. M., Sawa, B. A. Jaiyeoba, I. A. (2015). Multi-Temporal Remote Sensing of Land

Use Dynamics in Zaria, Nigeria. Journal of Environment and Earth Science, 5(9): 121-

138. ISSN 2224-3216.

Kang, J. H., Lee, S.W., Cho, K.H., Ki, S.J., Cha, S.M. and Kim, J.H. (2010). Linking land-use

type and stream water quality using spatial data of fecal indica- tor bacteria and heavy

metals in the Yeongsan River Basin. Water Res. 44: 4143–4157.

kugu, A. S. (2018). Urban Sprawl Pattern and Its Implications for Urban Management (Case

Study: Zaria Urban Area, Nigeria). International Journal of Architecture and Urban

Development, 8(4): 5-12.

Nzoiwu, C. P., Agulue, E. I., Mbah, S. Chidera P. Igboanugo, C. P. (2017). Impact of Land

Use/Land Cover Change on Surface Temperature Condition of Awka Town, Nigeria.

Journal of Geographic Information System, 9: 763-776.

https://doi.org/10.4236/jgis.2017.96047

National Population Commission, (2006). Population data for Zaria, Nigeria.

FUTY Journal of the Environment Vol. 14 No. 1 March, 2020

11

Okewu, A. A. (2016). Urban Induced Land Use/Land Cover Changes in Zaria, Kaduna State,

Nigeria. An MSc Dissertation submitted to the School of Postgraduate Studies, Ahmadu

Bello University, Zaria, Nigeria.

Oyinloye, M. A. and Kufoniyi, O. (2011). Analysis of Landuse, Landcover Change and Urban

Expansion in Akure, Nigeria. Journal of Innovative Research in Engineering and

Sciences 2(4): 234-248

Saini, V. and Tiwari, R. K. (2017). Effect of Urbanization On Land Surface Temperature and

Ndvi: A Case Study of Dehradun, India. Retrieved from

https:/www.researchgate.net/…/321824515

Takeuchi, W., Hashim, N. and Thet, K.M. (2010) Application of RS and GIS for Monitoring

UHI in KL Metropolitan Area. MAp Asia 2010 & ISG 2010. Kuala Lumpur.

United States Geological Survey, (2013).

http://landsat.usgs.gov/Landsat8_Using_Product.php.

Voogt, J. A. and Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote Sensing

of Environment, 86: 370 – 384.

United States Census Bureau, (2019). Population of Nigeria. Retrieved from

Census.gov.Glossary.

Zhang, Y., Dudgeon, D., Cheng, D., Thoe, W., Fok, L., Lee, J. H. W. and Wang, Z. (2010).

Impacts of land use and water quality on macroinvertebrate communities in the Pearl

river drainage basin, China. Hydrobiologia 652:71–88. DOI 10.1007/s10750-010-320-x.

© 2020 by the authors. License FUTY Journal of the Environment, Yola, Nigeria. This

article is an open access distributed under the terms and conditions of the Creative

Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).